- Email: [email protected]
Hyperactivity Disorder
A Meta-Analysis of Working Memory Impairments in Children With Attention-Deficit/Hyperactivity Disorder RHONDA MARTINUSSEN, M.ED., JILL HAYDEN, D.C., SHEILAH HOGG-JOHNSON, PH.D., AND ROSEMARY TANNOCK, PH.D.
ABSTRACT Objective: To determine the empirical evidence for deficits in working memory (WM) processes in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). Method: Exploratory meta-analytic procedures were used to investigate whether children with ADHD exhibit WM impairments. Twenty-six empirical research studies published from 1997 to December, 2003 (subsequent to a previous review) met our inclusion criteria. WM measures were categorized according to both modality (verbal, spatial) and type of processing required (storage versus storage/manipulation). Results: Children with ADHD exhibited deficits in multiple components of WM that were independent of comorbidity with language learning disorders and weaknesses in general intellectual ability. Overall effect sizes for spatial storage (effect size = 0.85, CI = 0.62 – 1.08) and spatial central executive WM (effect size = 1.06, confidence interval = 0.72–1.39) were greater than those obtained for verbal storage (effect size = 0.47, confidence interval = 0.36–0.59) and verbal central executive WM (effect size = 0.43, confidence interval = 0.24–0.62). Conclusion: Evidence of WM impairments in children with ADHD supports recent theoretical models implicating WM processes in ADHD. Future research is needed to more clearly delineate the nature, severity, and specificity of the impairments to ADHD. J. Am. Acad. Child Adolesc. Psychiatry, 2005;44(4):377–384. Key Words: attention-deficit/hyperactivity disorder, working memory, reading disorder.
Working memory (WM) is critical to conscious thought because it permits internal representation of information (e.g., rules) to guide decision making and overt behavior (responses) during an activity so that behavior is not dominated by the immediate sensory cues in the environment. Although several theoretical perspectives on WM exist (see Miyake and Shah [1999] for a review), Accepted November 2, 2004. Ms. Martinussen is with the Institute of Medical Science, University of Toronto; Dr. Hayden is with the University of Toronto and the Institute for Work and Health, Toronto; Dr. Hogg-Johnson is with the Institute for Work and Health, Toronto; Dr. Tannock is with The Hospital for Sick Children and the University of Toronto, Toronto. This research was funded by a Canadian Institute of Health Research (CIHR) Doctoral Fellowship (Ms. Martinussen), a CIHR Postdoctoral Fellowship (Dr. Hayden), and a CIHR operating grant (Dr. Tannock). Article Plus (online only) materials for this article appear on the Journal’s Web site: www.jaacap.com. Correspondence to Dr. Rosemary Tannock, Brain and Behavior Research, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, Canada M5G 1X8; e-mail: [email protected]. 0890-8567/05/4404–03772005 by the American Academy of Child and Adolescent Psychiatry. DOI: 10.1097/01.chi.0000153228.72591.73
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
the most influential model of WM is Baddeley’s (1986) multicomponent model, which is composed of both verbal and visuospatial storage systems and a central executive (CE) that regulates and controls the two storage systems. According to Baddeley’s (1986) model, the verbal storage component stores linguistic information, which is subject to rapid decay unless the information is maintained by a subvocal rehearsal process of the items. The visuospatial storage system maintains spatial information with shifts in spatial attention facilitating retention of location information (Awh et al., 2000). Deficits in verbal storage are associated with language acquisition weaknesses including vocabulary and word decoding (Baddeley et al., 1998; Swanson and Howell, 2001), whereas weaknesses in visuospatial storage have been associated with low academic achievement in literacy, comprehension, and arithmetic (Gathercole and Pickering, 2000). The CE component of WM is thought to control and manipulate the stored information as well as act on information retrieved from long-term memory (Baddeley, 1996) to support complex cognitive activities
377
MARTI NUSS EN ET AL.
such as mental calculation, language and reading comprehension, and text generation (Baddeley et al., 1998; Daneman and Carpenter, 1980; Daneman and Merikle, 1996; Gathercole and Pickering, 2000; Hoskyn and Swanson, 2003; Swanson, 1999). This multicomponent model of WM has been studied extensively and has received considerable support from a wide range of neuropsychological (see Baddeley [1996] for a review) and imaging studies (see Fletcher and Henson [2001] for a review). WM functions are thought to be highly dependent on frontostriatal brain regions (Bunge et al., 2001; Kondo et al., 2004; Lewis et al., 2004; Smith and Jonides, 1999) with recent data also implicating the cerebellum (Gottwald et al., 2003; Lalonde and Strazielle, 2003). Data also suggest that different neural structures are activated depending on the modality of the CE task, with verbal tasks more lateralized to the left and spatial to the right (see Fletcher and Henson [2001] for a review). Moreover, it has been demonstrated in both preclinical and clinical studies that dopaminergic and noradrenergic systems modulate WM processes (Arnsten, 2001; Goldman-Rakic et al., 2004). WM processes have been implicated in theoretical models of attention-deficit/hyperactivity disorder (ADHD) (Barkley, 1997; Rapport et al., 2001). This is not surprising given that converging data from neuropsychological and neuroimaging studies implicate frontostriatocerebellar dysfunctions in ADHD (Castellanos et al., 2002; Durston, 2003; Giedd et al., 2001). Finally, given the clinical efficacy of stimulant medications in treating ADHD, catecholamine dysregulation has been implicated in the etiology of ADHD (Biederman and Faraone, 2002; Levy and Swanson, 2001). Hence, children with ADHD may exhibit WM deficits because of dysfunction to frontostriatocerebellar brain circuits and/or because of dopaminergic dysregulation (Levy and Swanson, 2001). Notwithstanding the theoretical predictions of WM deficits in ADHD, the extant empirical evidence is equivocal. For example, a previous comprehensive review of executive functioning in ADHD concluded that there was no robust evidence of WM impairments in ADHD (Pennington and Ozonoff, 1996). However, only a few studies of WM were available at that time, and the majority assessed verbal WM processes or included tasks that could be encoded either verbally or visually. Since that time, an increasing number of
378
published studies have now assessed verbal and spatial WM processes in children with ADHD, but the results have been inconsistent (Barnett et al., 2001; Karatekin and Asarnow, 1998; Kempton et al., 1999; Kerns et al., 2001; Mariani and Barkley, 1997; McInnes et al., 2003; Oie et al., 1999; Shallice et al., 2002; Stevens et al., 2002 versus Adams and Snowling, 2001; Rucklidge and Tannock, 2002; Willcutt et al., 2001). We identified several possible reasons for the lack of consistency in findings. First, differences in results may be due to variations in either the modality (verbal versus spatial) or the processing requirements (storage versus storage/manipulation) of the WM tasks. Alternatively, findings may be inconsistent because studies may or may not control for potential confounding variables such as comorbid reading difficulties or language impairments (RD/LI) or general intellectual functioning. Comorbidity with language-based learning disorders is of particular concern for studies of verbal WM processes because several recent studies suggest that when comorbid RD/LI is controlled, children with ADHD may not exhibit impairments on verbal WM tasks (Rucklidge and Tannock, 2002; Toplak et al., 2003; Willcutt et al., 2001). Similarly, it is possible that WM impairments in children with ADHD may be attributable to general intellectual weaknesses (e.g., Barkley et al., 2001; Kuntsi et al., 2001; Tripp et al., 2002). Thus, the main goal of this study was to conduct a meta-analysis of existing data to determine whether children with ADHD exhibit a specific pattern of deficits related to either WM modality or level of processing (i.e., verbal versus spatial or storage versus CE). Second, we examined the impact of potential moderating variables (comorbidity with RD/LI, general intellectual functioning) on the findings obtained.
METHOD A literature search was conducted to identify published studies in which WM was assessed in children and adolescents with ADHD. Medline and PsycINFO were searched from 1997 to December 2003 (subsequent to a previous review by Pennington and Ozonoff [1996]) using combinations of the specific MeSH terms (ADHD, attention deficit disorder, hyperactivity) with the keywords (WM, verbal span, spatial span, short-term memory, phonological loop, visual-spatial sketchpad, digit span). The reference lists of the retrieved articles were also examined for additional relevant publications. We limited our search to all human studies involving children between the ages of 4 and 18 years that were published in the English language in peer-reviewed journals.
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05
WORKING MEMORY IMP AI RMENTS IN ADHD
Study Selection Inclusion and exclusion criteria were used to identify articles relevant to the review. Studies had to examine WM processes in children diagnosed with ADHD according to DSM-III, DSM-III-R, or DSM-IV criteria or in children who exhibited clinically significant symptoms of ADHD (e.g., inattention and/or hyperactivity) as measured with a validated screening instrument. At least one of the comparison groups had to be composed of normally developing children, and children in both the ADHD and comparison groups had to have a Full Scale IQ greater than 70. Each study had to include an assessment of at least one of the four WM components (verbal or spatial storage, verbal or spatial CE). The following definitions were used to classify tasks into one of four WM domains. Verbal or spatial storage measures had to require the short-term storage of temporally ordered linguistic (e.g., digit span, word span) or location information (e.g., spatial span, Corsi blocks) with immediate recall. Central executive WM measures were required to involve both maintenance and manipulation (e.g., updating, reordering) of either verbal or spatial stimuli (see Wager and Smith, 2003).
and larger values, suggesting that there is very little consistency in effect sizes across the studies. Metaregression techniques were used to examine two potential moderator variables: (1) control for comorbid RD/LI and (2) general intellectual functioning (IQ). Separate weighted metaregressions were conducted to examine the moderator variables within each WM domain. It is possible that group differences will be attenuated in studies that controlled for comorbid RD/LI and/or that matched ADHD and normal control samples on IQ. Sensitivity analyses were conducted to examine coding decisions and the influence of observed outliers on the pooled results. Publication bias was investigated using the method of Egger et al. (1997). Publication bias is suggested when the Egger et al. publication bias statistic is significantly greater than zero (p < .10). Analyses were conducted using Comprehensive Meta-Analysis (Borenstein and Rothstein, 2001) and STATA (StataCorp, 2003).
RESULTS
Data Abstraction
Study Characteristics
For each of the studies, we recorded the following information: age of sample, diagnostic criteria and procedures, control (i.e., exclusion of comorbid psychiatric or learning disorders) and matching (i.e., age, Full Scale IQ, reading and language) procedures, medication status of subjects, and instruments used to assess WM. A small proportion of studies used more than one measure of a particular component of WM. In each case, we averaged the effect sizes obtained from each WM measure in the study given the similarity in the measures (e.g., sentence span, counting span [Kuntsi et al., 2001; Willcutt et al., 2001]). In addition, if test results were given for subsections of the same test (e.g., Norrelgen et al., 1999), an average effect size was calculated across subsections. Finally, two studies (Schmitz et al., 2002; Warner-Rogers et al., 2000) examined the verbal storage performance of each of the three subtypes of ADHD. Here we used the data from the combined (overactive and inattentive) subgroup since it was most representative of the samples from the other studies.
Thirty-two studies were located, but only 26 studies met our inclusion criteria. (An appendix containing the descriptive characteristics of the studies is available on the Journal’s Web Site at www.jaacap.com via the ArticlePlus feature). The majority of the studies focused on children younger than the age of 13 years, with only six studies examining WM processes in adolescents. Diagnostic criteria varied considerably across the studies within each WM domain. Overall, 15 of the 26 studies used DSM-IV criteria, six used DSM-III-R criteria, one used DSM-III criteria, and the remaining four studies used an ADHD proxy variable (e.g., elevated hyperactivity ratings). Regarding control variables, the majority of studies matched ADHD and control groups for age and gender but differed in how well they controlled for confounding variables such as comorbid psychiatric and/or language learning disorders and differences in general intellectual functioning. Less than half of the total studies (10 of 26) either excluded comorbid language learning disorders from their ADHD sample or subdivided the ADHD sample into those with and without comorbid reading or language problems. Moreover, only seven studies systematically controlled for or examined the effect of comorbid psychiatric disorders such as oppositional defiant disorder, conduct disorder, or anxiety disorders (Barkley et al., 2001; Cairney et al., 2001; Kalff et al., 2002; Kuntsi et al., 2001; Seidman et al., 1997; Shallice et al., 2002; Wiers et al., 1998). Fourteen of the 26 studies matched the ADHD and normal comparison groups
Analysis Separate meta-analyses were conducted to synthesize the available data for each of the four WM domains. Effect sizes were calculated using Cohen’s d (Cohen, 1988) to quantify the magnitude of the difference in memory performance between the children with ADHD and normal comparison children on each WM measure. Pooling of study results within each domain was done using an inverse varianceweighted method of random effects analysis (DerSimonian and Laird, 1986). Effect sizes were interpreted according to Cohen’s (1988) guidelines (d = 0.20 is small; d = 0.50 is moderate; d = 0.80 is large). Visual inspection of the data was completed using Forrest plots, and any potential outliers were identified within each domain. For each meta-analysis, homogeneity testing was conducted to determine the extent to which there was variation in findings between studies within each domain. The I 2 statistic and 95% confidence intervals were calculated according to Higgins et al. (2003) to describe the amount of total variation across studies due to heterogeneity rather than chance. The range of values for I 2 lies between 0 (negative values are set to 0) and 100%, with a value of 0%, indicating no observed heterogeneity
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
379
MARTI NUSS EN ET AL.
on at least one component (e.g., verbal or nonverbal reasoning) of general intellectual functioning. Finally, medication status is a particularly important variable to control when studying WM in ADHD because stimulant medications have been found to improve WM processes in normal adults (Mehta et al., 2000) and in children with ADHD (Bedard et al., 2004; Tannock et al., 1995; Zeiner et al., 1999). Two studies (Warner-Rogers et al., 2000; Williams et al., 2000) did not indicate the medication status of the children with ADHD, and each of the remaining studies, except for Seidman et al. (1997), requested children to discontinue stimulant medication at least 24 hours before testing. Overall, the majority of studies were not confounded by medication use. WM Impairments in Children With ADHD. Results of the four meta-analyses are presented in Table 1. Children with ADHD exhibit moderate to large impairments in WM, with the magnitude of the impairment varying according to the modality of the WM task. Large impairments were evident in both the spatial storage and spatial CE domains, whereas more modest deficits were found in verbal storage and verbal CE domains. However, heterogeneity analyses revealed that each of the WM domains, except verbal storage, had moderate to high I 2 values (Higgins et al., 2003). This finding indicates that within each of these three domains, there was substantial variability in findings between studies that was not due to chance alone. Publication bias was not evident in the verbal storage or verbal or spatial CE domains (p > .10), but the Egger
et al. publication bias statistic was significant for the spatial storage domain (p = .03). Sensitivity Analyses
First, we addressed whether including the WISC-III digit span subtest (Wechsler, 1999) in the verbal storage category affected the findings for that domain. Digit span is derived from scores of two subtests: digits forward and digits backward. The first subtest clearly indexes verbal storage processes, whereas the reverse version requires the subject to recall the series of digits in reverse order, which entails manipulation of information. Results indicated that there were no differences in the findings for the verbal storage domain when the digit span subtest was excluded from the verbal storage analyses. Next, we examined the effect sizes within each WM category for outliers (Lipsey and Wilson, 2001). One of the studies (Mataro et al., 1997) in the verbal CE domain reported an effect size that was much greater than any other reported study within this domain. This study had been included in the verbal CE category because the Paced Auditory Serial Addition Test (PASAT) is generally considered a verbal WM measure. However, recent research examining the PASAT in an adult ADHD population demonstrated that the adults with ADHD were activating different areas than normal controls and were primarily completing the task using visual imagery (Schweitzer et al., 2000). Hence, it is possible that for the adolescents in the Mataro et al. (1997) study, the PASAT may have been assessing visuospatial rather
TABLE 1 Meta-Analyses of Differences in Working Memory Components Between Children With Attention-Deficit/Hyperactivity Disorder and Normal Comparison Subjects Memory Domain Verbal storage Verbal CE Spatial storage Spatial CE
No. of Subjects
No. of Studies
ADHD
NC
Standardized Mean Difference
t
95% CI
I 2 (95% CI)a
16 13 12b 9 8 7c
476 475 464 318 161 151
847 557 538 304 166 156
0.47 0.56 0.43 0.85 1.06 1.14
7.8* 4.1* 4.5* 7.2* 6.2* 7.0*
0.36–0.59 0.29–0.83 0.24–0.62 0.62–1.08 0.72–1.39 0.82–1.46
0 (0–52) 76 (58–86) 49 (1.5–73) 45 (0–75) 56 (5–80) 50 (0–79)
Note: CI = confidence interval; ADHD = attention-deficit/hyperactivity disorder; NC = normal controls; CE = central executive. Values of I 2 are percentages; 95% CI are calculated as proposed by Higgins and Thompson (2002). b Data with Mataro et al. (1997) study excluded. c Data with Williams et al. (2000) study excluded. *p < .001. a
380
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05
WORKING MEMORY IMP AI RMENTS IN ADHD
than verbal processing performance. Removing this study from the analyses reduced the heterogeneity of the verbal CE domain and resulted in a lower pooled effect size (Table 1). Similarly, in the visuospatial CE category, the effect size of the Williams et al. (2000) study was considerably smaller than any of the other studies in that category. This may have been due to the very young age of the participants (6 years). Hence, the Cambridge Neuropsychological Test Automated Battery (CANTAB) spatial WM test may have been less sensitive to frontal dysfunction in this age range. However, when this study is removed from the analyses, there was little change in the findings for this domain. Moderator Analysis
Control for RD/LI explained a significant amount of the variance for the spatial storage domain (b = .42, p < .05), and this moderator approached significance for the spatial CE (b = .47, p = .08) component. Larger effect sizes were associated with studies that controlled for RD/LI versus those studies that did not control for comorbid RD/LI. In contrast to the findings for the spatial WM domains, neither moderator variable (control for RD/LI, IQ) explained a significant amount of variance for either the verbal storage or verbal CE domains. Last, when the metaregressions were repeated without the studies designated as outliers, there were no changes to the findings in either the verbal or spatial storage domains or the verbal CE domain. However, the findings indicated that controlling for RD/LI did explain a significant amount of variance in the spatial CE domain (b = .69, p < .01) when the Williams et al. (2000) study was removed from the analysis. DISCUSSION
One of the advantages of meta-analysis is that it permits the pooling of results across studies and thus provides greater power to detect group differences. Overall, the findings suggest that WM processes are impaired in children with ADHD. Specifically, marked reductions in performance relative to normal controls were found for both the spatial storage and spatial CE WM components. In contrast, modest deficits were found for verbal storage and verbal CE WM domains. There are several possible explanations for the larger deficits ob-
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
served in spatial compared with verbal WM in ADHD. First, this pattern of findings may arise because spatial WM tasks tend to involve the right hemisphere (Kwon et al., 2002), which has been implicated in ADHD (see Giedd et al. [2001] for a review). It is also possible that the spatial tasks are simply more challenging than the verbal tasks because they may require processes that are less automated or familiar. Alternatively, it is possible that another disorder underlies the spatial WM weaknesses in ADHD. Approximately half of all children with ADHD may have motor difficulties consistent with developmental coordination disorder (Barkley et al., 1990; Piek et al., 1999; Pitcher et al., 2003). Developmental coordination disorder has been shown to be strongly associated with deficiencies in visuospatial processing (see Wilson and McKenzie [1998] for a review). Hence, future research is required to tease apart the nature of the WM impairments specifically associated with ADHD and whether they are attenuated by controlling for comorbid developmental coordination disorder. Heterogeneity statistics indicated that there was a moderate degree of inconsistency in the findings in each of the WM domains, except for verbal storage. Results of the metaregression analyses indicated that neither of the moderator variables (control for RD/LI, control for IQ) explained a significant amount of variation among studies in either the verbal storage or verbal CE domains of WM. Given the moderate degree of inconsistency of evidence in the verbal CE domain, this finding suggests that there must be other sources of variability within this domain that could not be addressed in the current study. For example, variation in findings in the verbal CE domain may be a result of the range of measures used to assess this construct. Although each of the verbal CE tasks required storage and manipulation, they differed in other factors such as linguistic or number knowledge. Hence, more research is needed that systematically examines how variation in verbal CE task components affects performance in children with ADHD. In contrast to the verbal domains, control for comorbid RD/LI was a significant moderator variable for both the spatial storage and spatial CE domains with greater effect sizes associated with studies that controlled for this confounder. This finding is surprising and somewhat difficult to explain because prior research has shown that children with RD/LI exhibit spatial storage
381
MARTI NUSS EN ET AL.
and spatial CE deficits independent of ADHD (Kaplan et al., 1998; McInnes et al., 2003). However, there are also data suggesting that spatial WM deficits are not characteristic of children with language impairments (Kushnir and Blake, 1996; Williams et al., 2000). More research is needed that examines the specific WM profiles associated with each disorder (ADHD, RD, LI) to better delineate the specific cognitive weaknesses associated with each disorder and areas of overlap. Nonetheless, the results provide support for WM deficits in ADHD, which in turn is consistent with both neuropsychological and neuroimaging data implicating frontostriatal dysfunctions in ADHD (Aman et al., 1998; Durston, 2003; Giedd et al., 2001) and recent findings linking ADHD with abnormalities in the dopamine system (Maher et al., 2002; Misener et al., 2004) as WM performance is highly dependent on prefrontal regions of the brain (Braver et al., 1997; Rypma and D’Esposito, 1999) and can be modulated by catecholamines (Bedard et al., 2004; Mattay et al., 2000). Future research should continue to explore the nature and severity of WM deficits in children with ADHD while also controlling for potential confounds such as comorbid motor, psychiatric, and/or learning disorders to examine the specificity of WM deficits to ADHD. Moreover, given that frontostriatal and cerebellar brain regions are implicated in ADHD (Durston, 2003) and in WM (Fletcher and Henson, 2001), it would be helpful to use neuroimaging techniques to examine whether alterations in the structure or function of these brain regions are related to subnormal performance on WM tasks in children with ADHD. Finally, further studies are needed that address the issue of symptom dimensions by investigating whether unique WM profiles are associated with specific symptom clusters. Limitations
Although a rigorous approach was used to review studies, this meta-analysis is constrained by the limitations that were inherent in the published study data. First, we were unable to examine whether comorbid psychiatric disorders or ADHD subtype differences moderated WM performance because too few studies examined these variables. Similarly, although there was variability in diagnostic criteria, the number of studies within each diagnostic category (e.g., DSM-IV, DSM-III-R, proxy ratings) was small, and therefore
382
this potentially confounding factor could not be analyzed statistically. Second, only published studies were included in the meta-analysis. Publication bias, which appeared to be evident in the spatial storage domain, may have increased our chances of finding larger effect sizes because unpublished studies tend to have smaller effect sizes than those that are published (Rosenthal, 1991). Overall, our findings should be interpreted with caution and regarded as exploratory in nature. Clinical Implications
Deficits in WM are known to compromise academic achievement (Gathercole and Pickering, 2000; Gathercole et al., 2004; Jarvis and Gathercole, 2003). Hence, the finding that children with ADHD exhibit moderate to large deficits in multiple components of WM has important implications for clinicians. Normal academic progress may be constrained by WM limitations because many typical academic activities depend heavily on WM (e.g., arithmetic problem solving, reading comprehension, text generation) (Daneman and Carpenter, 1980; Kellogg, 2001; Passolunghi and Siegel, 2001; Swanson and Berninger, 1996). Hence, it is possible that poor academic progress in children with ADHD may be the result of WM deficiencies rather than a direct consequence of the behavioral symptoms of inattention and/or hyperactivity-impulsivity (Rapport et al., 1999). WM constraints may also limit the efficacy of cognitively based treatment programs that are designed to target the behavioral symptoms of ADHD. In which case, specific accommodations may be required to help the child more easily encode, access, and retrieve information in an organized fashion. It is possible that providing support for WM limitations may help to reduce functional impairments in children with ADHD. According to Rosenshine (1997), effective teaching strategies such as teaching information in small steps followed with guided practice and using scaffolds such as cue cards may be successful because they appear to reduce the load on WM. Hence, these teaching strategies may facilitate academic success in children with ADHD. In addition, Dawson and Guare (2004) suggest that children with WM weaknesses may benefit from external support systems (e.g., visual cues, checklists, coaching) to help them remember specific goals and procedures. Additional research is needed that examines the relationship between WM limitations and
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05
WORKING MEMORY IMP AI RMENTS IN ADHD
academic achievement in children with ADHD to better understand the role that WM may play in the scholastic weaknesses exhibited by many children with ADHD (Barkley, 1990). Disclosure: Dr. Tannock has received research funding from, is a consultant for, and is on the speaker’s bureau for Eli Lilly. She is also on the advisory board for Shire and has received funding (for placebo control) from Novartis. The other authors have no financial relationships to disclose. REFERENCES References marked with an asterisk indicate studies included in the metaanalysis. *Adams JW, Snowling MJ (2001), Executive function and reading impairments in children reported by their teachers as Ôhyperactive.Õ Br J Dev Psychol 19:293–306 Aman CJ, Roberts RJ, Pennington BF (1998), A neuropsychological examination of the underlying deficit in attention deficit hyperactivity disorder: frontal lobe versus right parietal lobe theories. Dev Psychol 34: 956–969 Arnsten AFT (2001), Dopaminergic and noradrenergic influences on cognitive functions mediated by prefrontal cortex. In: Stimulant Drugs and ADHD: Basic and Clinical Neuroscience, Solanto MV, Arnsten AF, Castellanos FX, eds. London: Oxford University Press, pp 185–208 Awh E, Anllo-Vento L, Hillyard SA (2000), The role of spatial selective attention in working memory for locations: evidence from event-related potentials. J Cogn Neurosci 12:840–847 Baddeley A (1986), Working Memory. Oxford, England: Oxford University Press Baddeley A (1996), The fractionation of working memory. Proc Natl Acad Sci U S A 93:13468–13472 Baddeley A, Gathercole SE, Papagno C (1998), The phonological loop as a language learning device. Psychol Rev 105:158–173 Barkley R (1990), Attention-Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment. New York: Guilford Barkley R, DuPaul GJ, McMurray MB (1990), Comprehensive evaluation of attention deficit disorder with and without hyperactivity as defined by research criteria. J Consult Clin Psychol 58:775–789 Barkley R (1997), Behavioral inhibition, sustained attention, and executive function: constructing a unified theory of ADHD. Psychol Bull 121:65–94 *Barkley RA, Edwards G, Laneri M, Fletcher K, Metevia L (2001), Executive functioning, temporal discounting, and sense of time in adolescents with attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). J Abnorm Child Psychol 29:541–556 Barnett R, Maruff P, Vance A et al. (2001), Abnormal executive function in attention deficit hyperactivity disorder: the effect of stimulant medication and age on spatial working memory. Psychol Med 31:1107–1115 Bedard AC, Martinussen R, Ickowicz A, Tannock R (2004), Methylphenidate improves visual-spatial memory in children with attention deficit hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 43: 260–268 Biederman J, Faraone SV (2002), Current concepts on the neurobiology of ADHD. J Atten Disord 6:S7–16 Borenstein M, Rothstein H (2001) Comprehensive Meta-Analysis: Version 1.0. Englewood, NJ: Biostat Braver TS, Cohen JD, Nystrom LE, Jonides J, Smith EE, Noll DC (1997), A parametric study of prefrontal cortex involvement in human working memory. Neuroimage 5:49–62 Bunge SA, Ochsner KN, Desmond JE, Glover GH, Gabrieli JD (2001), Prefrontal regions are involved in keeping information in and out of mind. Brain 124:2074–2086
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
*Cairney S, Maruff P, Vance A, Barnett R, Luk E, Currie J (2001), Contextual abnormalities of saccadic inhibition in children with attention deficit hyperactivity disorder. Exp Brain Res 141:507–518 Castellanos FX, Lee PP, Sharp W et al. (2002), Developmental trajectories of brain volume abnormalities in children and adolescents with attentiondeficit/hyperactivity disorder. JAMA 288:1740–1748 Cohen J (1988), Statistical Power Analysis for the Behavioral Sciences. rev. ed. New York: Academic Daneman M, Carpenter PA (1980), Individual differences in working memory and reading. J Verb Learn Verb Behav 19:450–466 Daneman M, Merikle PM (1996), Working memory and language comprehension: a meta-analysis. Psychon Bull Rev 3:422–433 Dawson P, Guare R (2004), Executive Skills in Children and Adolescents: A Practical Guide to Assessment and Intervention. New York: Guilford DerSimonian R, Laird N (1986), Meta-analysis in clinical trials. Control Clin Trials 7:177–188 Durston S (2003), A review of the biological bases of ADHD: what have we learned from imaging studies? Ment Retard Dev Disabil Res Rev 9:184–195 Egger M, Smith GD, Schneider M, Minder C (1997), Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634 Fletcher PC, Henson RNA (2001), Frontal lobes and human memory: insights from functional neuroimaging. Brain 124:849–881 Gathercole SE, Pickering SJ (2000), Working memory deficits in children with low achievements in the national curriculum at 7 years of age. Br J Educ Psychol 70:177–194 Gathercole SE, Pickering SJ, Knight C, Stegman Z (2004), Working memory skills and educational attainment: evidence from national curriculum assessments at 7 and 14 years of age. Appl Cogn Psychol 18:1–16 Giedd JN, Blumenthal J, Molloy E, Castellanos FX (2001), Brain imaging of attention deficit hyperactivity disorder. Ann N Y Acad Sci 931:33–49 Goldman-Rakic PS, Castener SA, Svensson TH, Siever LJ, Williams GV (2004), Targeting the dopamine D (1) receptor in schizophrenia: insights for cognitive dysfunction. Psychopharmacol (Berl) 174:3–16 Gottwald B, Mihajlovic A, Wilde B, Mehdorn HM (2003), Does the cerebellum contribute to specific aspects of attention? Neuropsychologia 41:1452–1460 Higgins JPT, Thompson SG (2002), Quantifying heterogeneity in a metaanalysis. Stat Med 21:1539–1558 Higgins JPT, Thompson SG, Deeks JJ, Altman DG (2003), Measuring inconsistency in meta-analyses. BMJ 327:557–560 Hoskyn M, Swanson HL (2003), The relationship between working memory and writing in younger and older adults. Reading Writing 16: 759–784 Jarvis HL, Gathercole SE (2003), Verbal and non-verbal working memory and achievements on national curriculum tests at 11 and 14 years. Educ Child Psychol 20:123–140 *Kalff AC, Hendriksen JGM, Kroes M et al. (2002), Neurocognitive performance of 5- and 6-year old children who met criteria for attention deficit/hyperactivity disorder at 18 months follow-up: results from a prospective longitudinal study. J Abnorm Child Psychol 30:589–598 *Kaplan BJ, Dewey D, Crawford SG, Fisher GC (1998), Deficits in longterm memory are not characteristic of ADHD. J Clin Exp Neuropsychol 20:518–528 *Karatekin C, Asarnow RF (1998), Working memory in childhood-onset schizophrenia and attention-deficit/hyperactivity disorder. Psychiatry Res 80:165–176 Kellogg RT (2001), Competition for working memory among writing processes. Am J Psychol 114:175–191 *Kempton S, Vance A, Maruff P, Luk E, Costin J, Pantelis C (1999), Executive function and attention deficit hyperactivity disorder: stimulant medication and better executive function performance in children. Psychol Med 29:527–538 *Kerns K, McInerney RJ, Wilde NJ (2001), Time reproduction, working memory, and behavioral inhibition in children with ADHD. Neuropsychol Dev Cogn Sect C Child Neuropsychol 7:21–31 Kondo H, Morishita M, Osaka N, Osaka M (2004), Functional roles of the cingulo-frontal network in performance on working memory. Neuroimage 21:2–14
383
MARTI NUSS EN ET AL.
*Kuntsi J, Oosterlaan J, Stevenson J (2001), Psychological mechanisms in hyperactivity: I Response inhibition deficit, working memory impairment, delay aversion or something else? J Child Psychol Psychiatry 42:199–210 Kushnir CC, Blake J (1996), The nature of the cognitive deficit in specific language impairment. First Language 16:21–40 Kwon H, Reiss AL, Menon V (2002), Neural basis of protracted developmental changes in visuo-spatial working memory Proc Natl Acad Sci U S A 99:13336–13341 Lalonde R, Strazielle C (2003), The effects of cerebellar damage on maze learning in animals. Cerebellum 2:300–309 Lewis SJ, Dove A, Robbins TW, Barker RA, Owen AM (2004), Striatal contributions to working memory: a functional magnetic imaging study in humans. Eur J Neurosci 19:755–760 Levy F, Swanson JM (2001), Timing, space, and ADHD: the dopamine theory revisited. Aust N Z J Psychiatry 35:504–511 Lipsey MW, Wilson DB (2001), Practical Meta-Analysis. Thousand Oaks, CA: Sage Maher BS, Marazita ML, Ferrell RE, Vanyukov MM (2002), Dopamine system genes and attention deficit hyperactivity disorder: a meta-analysis. Psychiatr Genet 12:207–215 Mattay VS, Callicott JH, Bertolino A et al. (2000), Effects of dextroamphetamine on cognitive performance and cortical activation. Neuroimage 12L:268–275 *Mariani MA, Barkley RA (1997), Neuropsychological and academic functioning in preschool boys with attention deficit hyperactivity disorder. Dev Neuropsychol 13:111–129 *Mataro M, Garcia-Sanchez C, Junque C, Estevez-Gonzalez A, Pujol J (1997), Magnetic resonance imaging measurement of the caudate nucleus in adolescents with ADHD and its relationship with neuropsychological and behavioural measures. Arch Neurol 54:963–968 *McInnes A, Humphries T, Hogg-Johnson S, Tannock R (2003), Listening comprehension and working memory are impaired in attention-deficit hyperactivity disorder irrespective of language impairment. J Abnorm Child Psychol 31:427–443 Mehta MA, Owen A, Shakian BJ, Mavaddat N, Pickard JD, Robbins TW (2000), Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human brain. J Neurosci 20:RC65:1–6 Misener VL, Luca P, Azeke O et al. (2004), Linkage of the dopamine receptor D1 gene to attention-deficit/hyperactivity disorder. Mol Psychiatry 9:500–509 Miyake A, Shah P (1999), Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York: Cambridge University Press *Norrelgen F, Lacerda F, Forssberg H (1999), Speech discrimination and phonological working memory in children with ADHD. Dev Med Child Neurol 41:335–339 *Oie M, Sundet K, Rund BR (1999), Contrasts in memory functions between adolescents with schizophrenia or ADHD. Neuropsychologia 37:1351–1358 Passolunghi MC, Siegel LS (2001), Short-term memory, working memory, and inhibitory control in children with difficulties in arithmetic problem solving. J Exp Child Psychol 80:44–57 Pennington BF, Ozonoff S (1996), Executive functions and developmental psychopathology. J Child Psychol Psychiatry 37:51–87 Piek JP, Pitcher TM, Hay DA (1999), Motor coordination and kinaesthesis in males with attention-deficit hyperactivity disorder. Dev Med Child Neurol 41:159–165 Pitcher TM, Piek JP, Hay DA (2003), Fine and gross motor ability in males with ADHD. Dev Med Child Neurol 45:525–535 Rapport MD, Scanlan S, Denney CB (1999), Attention-deficit/hyperactivity disorder and scholastic achievement: a model of dual developmental pathways. J Child Psychol Psychiatry 40:169–184 Rapport MD, Chun K, Shore G, Isaacs P (2001), A conceptual model of child psychopathology: implications for understanding attention deficit hyperactivity disorder and treatment efficacy. J Clin Child Psychol 30:48–58 Rosenshine B (1997), Advances in research on instruction. In: Issues in Educating Students With Disabilities, Lloyd JW, Kameanui EJ, Chard D, eds. Mahwah, NJ: Erlbaum, pp 197–221
384
Rosenthal R (1991), Meta-analysis: a review. Psychosom Med 53:247–271 *Rucklidge J, Tannock R (2002), Neuropsychological profiles of adolescents with ADHD: effects of reading difficulties and gender. J Child Psychol Psychiatry 43:988–1003 Rypma B, D’Esposito M (1999), The roles of the prefrontal brain regions in components of working memory: effects of memory load and individual differences. Proc Natl Acad Sci U S A 96:6558–6563 *Schmitz M, Cadore L, Paczko M et al. (2002), Neuropsychological performance in DSM-IV ADHD subtypes: an exploratory study with untreated adolescents. Can J Psychiatry 47:863–869 Schweitzer JB, Faber TL, Grafton ST, Tune LE, Hoffmann JM, Kilts CD (2000), Alterations in the functional anatomy of working memory in adult attention deficit hyperactivity disorder. Am J Psychiatry 157:278–280 *Seidman LJ, Biederman J, Faraone SV, Weber W, Mennin D, Jones J (1997), A pilot study of neuropsychological function in girls with ADHD. J Am Acad Child Adolesc Psychiatry 36:366–373 *Shallice T, Marzocchi GM, Coser S, Del Savio M, Meuter RF, Rumiati R (2002), Executive function profile of children with attention deficit hyperactivity disorder. Dev Neuropsychol 21:43–71 Smith EE, Jonides J (1999), Storage and executive processes in the frontal lobes. Science 283:1657–1661 StataCorp (2003), Stata Statistical Software: Release 8.0. College Station, TX: Stata Corporation *Stevens J, Quittner AL, Zuckerman JB, Moore S (2002), Behavioral inhibition, self- regulation of motivation, and working memory in children with attention-deficit hyperactivity disorder. Dev Neuropsychol 21:117–139 Swanson HL, Berninger VW (1996), Individual differences in children’s working memory and writing skill. J Exp Child Psychol 63:358–385 Swanson HL (1999), Reading comprehension and working memory in learning-disabled readers: is the phonological loop more important than the executive system? J Exp Child Psychol 72:1–31 Swanson HL, Howell M (2001), Working memory, short-term memory, and speech rate as predictors of children’s reading performance at different ages. J Educ Psychol 93:720–734 Tannock R, Ickowicz A, Schachar R (1995), Differential effects of methylphenidate on working memory in ADHD children with or without comorbid anxiety. J Am Acad Child Adolesc Psychiatry 34:886–896 *Toplak ME, Rucklidge JJ, Hetherington R, John SCF, Tannock R (2003), Time perception deficits in attention-deficit/hyperactivity disorder and comorbid reading difficulties in child and adolescent samples. J Child Psychol Psychiatry 44:888–903 *Tripp G, Ryan J, Peace K (2002), Neuropsychological functioning in children with DSM-IV combined type attention deficit hyperactivity disorder. Aust N Z J Psychiatry 36:771–779 Wager TD, Smith EE (2003), Neuroimaging studies of working memory: a meta-analysis. Cogn Affect Behav Neurosci 3:255–274 *Warner-Rogers J, Taylor A, Taylor E, Sandberg S (2000), Inattentive behavior in childhood: epidemiology and implications for development. J Learn Disabil 33:520–536 Wechsler D (1999), Wechsler Intelligence Scale for Children. New York: Psychological Corporation *Wiers RW, Gunning WB, Sergeant JA (1998), Is a mild deficit in executive functions in boys related to childhood ADHD or to parental multigenerational alcoholism? J Abnorm Child Psychol 26:415–430 *Willcutt EG, Pennington BF, Boada R et al. (2001), A comparison of the cognitive deficits in reading disability and attention-deficit/hyperactivity disorder. J Abnorm Psychol 110:157–172 *Williams D, Stott CM, Goodyer IM, Sahakian BJ (2000), Specific language impairment with or without hyperactivity: neuropsychological evidence for frontostriatal dysfunction. Dev Med Child Neurol 42:368–375 Wilson PH, McKenzie BE (1998), Information processing deficits associated with developmental coordination disorder: a meta-analysis of research findings. J Child Psychol Psychiatry 29:829–840 Zeiner P, Bryhn G, Bjercke C, Truyen K, Strand G (1999), Response to methylphenidate in boys with attention-deficit hyperactivity disorder. Acta Paediatr 88:298–303
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05
ABSTRACT Objective: To determine the empirical evidence for deficits in working memory (WM) processes in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). Method: Exploratory meta-analytic procedures were used to investigate whether children with ADHD exhibit WM impairments. Twenty-six empirical research studies published from 1997 to December, 2003 (subsequent to a previous review) met our inclusion criteria. WM measures were categorized according to both modality (verbal, spatial) and type of processing required (storage versus storage/manipulation). Results: Children with ADHD exhibited deficits in multiple components of WM that were independent of comorbidity with language learning disorders and weaknesses in general intellectual ability. Overall effect sizes for spatial storage (effect size = 0.85, CI = 0.62 – 1.08) and spatial central executive WM (effect size = 1.06, confidence interval = 0.72–1.39) were greater than those obtained for verbal storage (effect size = 0.47, confidence interval = 0.36–0.59) and verbal central executive WM (effect size = 0.43, confidence interval = 0.24–0.62). Conclusion: Evidence of WM impairments in children with ADHD supports recent theoretical models implicating WM processes in ADHD. Future research is needed to more clearly delineate the nature, severity, and specificity of the impairments to ADHD. J. Am. Acad. Child Adolesc. Psychiatry, 2005;44(4):377–384. Key Words: attention-deficit/hyperactivity disorder, working memory, reading disorder.
Working memory (WM) is critical to conscious thought because it permits internal representation of information (e.g., rules) to guide decision making and overt behavior (responses) during an activity so that behavior is not dominated by the immediate sensory cues in the environment. Although several theoretical perspectives on WM exist (see Miyake and Shah [1999] for a review), Accepted November 2, 2004. Ms. Martinussen is with the Institute of Medical Science, University of Toronto; Dr. Hayden is with the University of Toronto and the Institute for Work and Health, Toronto; Dr. Hogg-Johnson is with the Institute for Work and Health, Toronto; Dr. Tannock is with The Hospital for Sick Children and the University of Toronto, Toronto. This research was funded by a Canadian Institute of Health Research (CIHR) Doctoral Fellowship (Ms. Martinussen), a CIHR Postdoctoral Fellowship (Dr. Hayden), and a CIHR operating grant (Dr. Tannock). Article Plus (online only) materials for this article appear on the Journal’s Web site: www.jaacap.com. Correspondence to Dr. Rosemary Tannock, Brain and Behavior Research, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, Canada M5G 1X8; e-mail: [email protected]. 0890-8567/05/4404–03772005 by the American Academy of Child and Adolescent Psychiatry. DOI: 10.1097/01.chi.0000153228.72591.73
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
the most influential model of WM is Baddeley’s (1986) multicomponent model, which is composed of both verbal and visuospatial storage systems and a central executive (CE) that regulates and controls the two storage systems. According to Baddeley’s (1986) model, the verbal storage component stores linguistic information, which is subject to rapid decay unless the information is maintained by a subvocal rehearsal process of the items. The visuospatial storage system maintains spatial information with shifts in spatial attention facilitating retention of location information (Awh et al., 2000). Deficits in verbal storage are associated with language acquisition weaknesses including vocabulary and word decoding (Baddeley et al., 1998; Swanson and Howell, 2001), whereas weaknesses in visuospatial storage have been associated with low academic achievement in literacy, comprehension, and arithmetic (Gathercole and Pickering, 2000). The CE component of WM is thought to control and manipulate the stored information as well as act on information retrieved from long-term memory (Baddeley, 1996) to support complex cognitive activities
377
MARTI NUSS EN ET AL.
such as mental calculation, language and reading comprehension, and text generation (Baddeley et al., 1998; Daneman and Carpenter, 1980; Daneman and Merikle, 1996; Gathercole and Pickering, 2000; Hoskyn and Swanson, 2003; Swanson, 1999). This multicomponent model of WM has been studied extensively and has received considerable support from a wide range of neuropsychological (see Baddeley [1996] for a review) and imaging studies (see Fletcher and Henson [2001] for a review). WM functions are thought to be highly dependent on frontostriatal brain regions (Bunge et al., 2001; Kondo et al., 2004; Lewis et al., 2004; Smith and Jonides, 1999) with recent data also implicating the cerebellum (Gottwald et al., 2003; Lalonde and Strazielle, 2003). Data also suggest that different neural structures are activated depending on the modality of the CE task, with verbal tasks more lateralized to the left and spatial to the right (see Fletcher and Henson [2001] for a review). Moreover, it has been demonstrated in both preclinical and clinical studies that dopaminergic and noradrenergic systems modulate WM processes (Arnsten, 2001; Goldman-Rakic et al., 2004). WM processes have been implicated in theoretical models of attention-deficit/hyperactivity disorder (ADHD) (Barkley, 1997; Rapport et al., 2001). This is not surprising given that converging data from neuropsychological and neuroimaging studies implicate frontostriatocerebellar dysfunctions in ADHD (Castellanos et al., 2002; Durston, 2003; Giedd et al., 2001). Finally, given the clinical efficacy of stimulant medications in treating ADHD, catecholamine dysregulation has been implicated in the etiology of ADHD (Biederman and Faraone, 2002; Levy and Swanson, 2001). Hence, children with ADHD may exhibit WM deficits because of dysfunction to frontostriatocerebellar brain circuits and/or because of dopaminergic dysregulation (Levy and Swanson, 2001). Notwithstanding the theoretical predictions of WM deficits in ADHD, the extant empirical evidence is equivocal. For example, a previous comprehensive review of executive functioning in ADHD concluded that there was no robust evidence of WM impairments in ADHD (Pennington and Ozonoff, 1996). However, only a few studies of WM were available at that time, and the majority assessed verbal WM processes or included tasks that could be encoded either verbally or visually. Since that time, an increasing number of
378
published studies have now assessed verbal and spatial WM processes in children with ADHD, but the results have been inconsistent (Barnett et al., 2001; Karatekin and Asarnow, 1998; Kempton et al., 1999; Kerns et al., 2001; Mariani and Barkley, 1997; McInnes et al., 2003; Oie et al., 1999; Shallice et al., 2002; Stevens et al., 2002 versus Adams and Snowling, 2001; Rucklidge and Tannock, 2002; Willcutt et al., 2001). We identified several possible reasons for the lack of consistency in findings. First, differences in results may be due to variations in either the modality (verbal versus spatial) or the processing requirements (storage versus storage/manipulation) of the WM tasks. Alternatively, findings may be inconsistent because studies may or may not control for potential confounding variables such as comorbid reading difficulties or language impairments (RD/LI) or general intellectual functioning. Comorbidity with language-based learning disorders is of particular concern for studies of verbal WM processes because several recent studies suggest that when comorbid RD/LI is controlled, children with ADHD may not exhibit impairments on verbal WM tasks (Rucklidge and Tannock, 2002; Toplak et al., 2003; Willcutt et al., 2001). Similarly, it is possible that WM impairments in children with ADHD may be attributable to general intellectual weaknesses (e.g., Barkley et al., 2001; Kuntsi et al., 2001; Tripp et al., 2002). Thus, the main goal of this study was to conduct a meta-analysis of existing data to determine whether children with ADHD exhibit a specific pattern of deficits related to either WM modality or level of processing (i.e., verbal versus spatial or storage versus CE). Second, we examined the impact of potential moderating variables (comorbidity with RD/LI, general intellectual functioning) on the findings obtained.
METHOD A literature search was conducted to identify published studies in which WM was assessed in children and adolescents with ADHD. Medline and PsycINFO were searched from 1997 to December 2003 (subsequent to a previous review by Pennington and Ozonoff [1996]) using combinations of the specific MeSH terms (ADHD, attention deficit disorder, hyperactivity) with the keywords (WM, verbal span, spatial span, short-term memory, phonological loop, visual-spatial sketchpad, digit span). The reference lists of the retrieved articles were also examined for additional relevant publications. We limited our search to all human studies involving children between the ages of 4 and 18 years that were published in the English language in peer-reviewed journals.
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05
WORKING MEMORY IMP AI RMENTS IN ADHD
Study Selection Inclusion and exclusion criteria were used to identify articles relevant to the review. Studies had to examine WM processes in children diagnosed with ADHD according to DSM-III, DSM-III-R, or DSM-IV criteria or in children who exhibited clinically significant symptoms of ADHD (e.g., inattention and/or hyperactivity) as measured with a validated screening instrument. At least one of the comparison groups had to be composed of normally developing children, and children in both the ADHD and comparison groups had to have a Full Scale IQ greater than 70. Each study had to include an assessment of at least one of the four WM components (verbal or spatial storage, verbal or spatial CE). The following definitions were used to classify tasks into one of four WM domains. Verbal or spatial storage measures had to require the short-term storage of temporally ordered linguistic (e.g., digit span, word span) or location information (e.g., spatial span, Corsi blocks) with immediate recall. Central executive WM measures were required to involve both maintenance and manipulation (e.g., updating, reordering) of either verbal or spatial stimuli (see Wager and Smith, 2003).
and larger values, suggesting that there is very little consistency in effect sizes across the studies. Metaregression techniques were used to examine two potential moderator variables: (1) control for comorbid RD/LI and (2) general intellectual functioning (IQ). Separate weighted metaregressions were conducted to examine the moderator variables within each WM domain. It is possible that group differences will be attenuated in studies that controlled for comorbid RD/LI and/or that matched ADHD and normal control samples on IQ. Sensitivity analyses were conducted to examine coding decisions and the influence of observed outliers on the pooled results. Publication bias was investigated using the method of Egger et al. (1997). Publication bias is suggested when the Egger et al. publication bias statistic is significantly greater than zero (p < .10). Analyses were conducted using Comprehensive Meta-Analysis (Borenstein and Rothstein, 2001) and STATA (StataCorp, 2003).
RESULTS
Data Abstraction
Study Characteristics
For each of the studies, we recorded the following information: age of sample, diagnostic criteria and procedures, control (i.e., exclusion of comorbid psychiatric or learning disorders) and matching (i.e., age, Full Scale IQ, reading and language) procedures, medication status of subjects, and instruments used to assess WM. A small proportion of studies used more than one measure of a particular component of WM. In each case, we averaged the effect sizes obtained from each WM measure in the study given the similarity in the measures (e.g., sentence span, counting span [Kuntsi et al., 2001; Willcutt et al., 2001]). In addition, if test results were given for subsections of the same test (e.g., Norrelgen et al., 1999), an average effect size was calculated across subsections. Finally, two studies (Schmitz et al., 2002; Warner-Rogers et al., 2000) examined the verbal storage performance of each of the three subtypes of ADHD. Here we used the data from the combined (overactive and inattentive) subgroup since it was most representative of the samples from the other studies.
Thirty-two studies were located, but only 26 studies met our inclusion criteria. (An appendix containing the descriptive characteristics of the studies is available on the Journal’s Web Site at www.jaacap.com via the ArticlePlus feature). The majority of the studies focused on children younger than the age of 13 years, with only six studies examining WM processes in adolescents. Diagnostic criteria varied considerably across the studies within each WM domain. Overall, 15 of the 26 studies used DSM-IV criteria, six used DSM-III-R criteria, one used DSM-III criteria, and the remaining four studies used an ADHD proxy variable (e.g., elevated hyperactivity ratings). Regarding control variables, the majority of studies matched ADHD and control groups for age and gender but differed in how well they controlled for confounding variables such as comorbid psychiatric and/or language learning disorders and differences in general intellectual functioning. Less than half of the total studies (10 of 26) either excluded comorbid language learning disorders from their ADHD sample or subdivided the ADHD sample into those with and without comorbid reading or language problems. Moreover, only seven studies systematically controlled for or examined the effect of comorbid psychiatric disorders such as oppositional defiant disorder, conduct disorder, or anxiety disorders (Barkley et al., 2001; Cairney et al., 2001; Kalff et al., 2002; Kuntsi et al., 2001; Seidman et al., 1997; Shallice et al., 2002; Wiers et al., 1998). Fourteen of the 26 studies matched the ADHD and normal comparison groups
Analysis Separate meta-analyses were conducted to synthesize the available data for each of the four WM domains. Effect sizes were calculated using Cohen’s d (Cohen, 1988) to quantify the magnitude of the difference in memory performance between the children with ADHD and normal comparison children on each WM measure. Pooling of study results within each domain was done using an inverse varianceweighted method of random effects analysis (DerSimonian and Laird, 1986). Effect sizes were interpreted according to Cohen’s (1988) guidelines (d = 0.20 is small; d = 0.50 is moderate; d = 0.80 is large). Visual inspection of the data was completed using Forrest plots, and any potential outliers were identified within each domain. For each meta-analysis, homogeneity testing was conducted to determine the extent to which there was variation in findings between studies within each domain. The I 2 statistic and 95% confidence intervals were calculated according to Higgins et al. (2003) to describe the amount of total variation across studies due to heterogeneity rather than chance. The range of values for I 2 lies between 0 (negative values are set to 0) and 100%, with a value of 0%, indicating no observed heterogeneity
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
379
MARTI NUSS EN ET AL.
on at least one component (e.g., verbal or nonverbal reasoning) of general intellectual functioning. Finally, medication status is a particularly important variable to control when studying WM in ADHD because stimulant medications have been found to improve WM processes in normal adults (Mehta et al., 2000) and in children with ADHD (Bedard et al., 2004; Tannock et al., 1995; Zeiner et al., 1999). Two studies (Warner-Rogers et al., 2000; Williams et al., 2000) did not indicate the medication status of the children with ADHD, and each of the remaining studies, except for Seidman et al. (1997), requested children to discontinue stimulant medication at least 24 hours before testing. Overall, the majority of studies were not confounded by medication use. WM Impairments in Children With ADHD. Results of the four meta-analyses are presented in Table 1. Children with ADHD exhibit moderate to large impairments in WM, with the magnitude of the impairment varying according to the modality of the WM task. Large impairments were evident in both the spatial storage and spatial CE domains, whereas more modest deficits were found in verbal storage and verbal CE domains. However, heterogeneity analyses revealed that each of the WM domains, except verbal storage, had moderate to high I 2 values (Higgins et al., 2003). This finding indicates that within each of these three domains, there was substantial variability in findings between studies that was not due to chance alone. Publication bias was not evident in the verbal storage or verbal or spatial CE domains (p > .10), but the Egger
et al. publication bias statistic was significant for the spatial storage domain (p = .03). Sensitivity Analyses
First, we addressed whether including the WISC-III digit span subtest (Wechsler, 1999) in the verbal storage category affected the findings for that domain. Digit span is derived from scores of two subtests: digits forward and digits backward. The first subtest clearly indexes verbal storage processes, whereas the reverse version requires the subject to recall the series of digits in reverse order, which entails manipulation of information. Results indicated that there were no differences in the findings for the verbal storage domain when the digit span subtest was excluded from the verbal storage analyses. Next, we examined the effect sizes within each WM category for outliers (Lipsey and Wilson, 2001). One of the studies (Mataro et al., 1997) in the verbal CE domain reported an effect size that was much greater than any other reported study within this domain. This study had been included in the verbal CE category because the Paced Auditory Serial Addition Test (PASAT) is generally considered a verbal WM measure. However, recent research examining the PASAT in an adult ADHD population demonstrated that the adults with ADHD were activating different areas than normal controls and were primarily completing the task using visual imagery (Schweitzer et al., 2000). Hence, it is possible that for the adolescents in the Mataro et al. (1997) study, the PASAT may have been assessing visuospatial rather
TABLE 1 Meta-Analyses of Differences in Working Memory Components Between Children With Attention-Deficit/Hyperactivity Disorder and Normal Comparison Subjects Memory Domain Verbal storage Verbal CE Spatial storage Spatial CE
No. of Subjects
No. of Studies
ADHD
NC
Standardized Mean Difference
t
95% CI
I 2 (95% CI)a
16 13 12b 9 8 7c
476 475 464 318 161 151
847 557 538 304 166 156
0.47 0.56 0.43 0.85 1.06 1.14
7.8* 4.1* 4.5* 7.2* 6.2* 7.0*
0.36–0.59 0.29–0.83 0.24–0.62 0.62–1.08 0.72–1.39 0.82–1.46
0 (0–52) 76 (58–86) 49 (1.5–73) 45 (0–75) 56 (5–80) 50 (0–79)
Note: CI = confidence interval; ADHD = attention-deficit/hyperactivity disorder; NC = normal controls; CE = central executive. Values of I 2 are percentages; 95% CI are calculated as proposed by Higgins and Thompson (2002). b Data with Mataro et al. (1997) study excluded. c Data with Williams et al. (2000) study excluded. *p < .001. a
380
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05
WORKING MEMORY IMP AI RMENTS IN ADHD
than verbal processing performance. Removing this study from the analyses reduced the heterogeneity of the verbal CE domain and resulted in a lower pooled effect size (Table 1). Similarly, in the visuospatial CE category, the effect size of the Williams et al. (2000) study was considerably smaller than any of the other studies in that category. This may have been due to the very young age of the participants (6 years). Hence, the Cambridge Neuropsychological Test Automated Battery (CANTAB) spatial WM test may have been less sensitive to frontal dysfunction in this age range. However, when this study is removed from the analyses, there was little change in the findings for this domain. Moderator Analysis
Control for RD/LI explained a significant amount of the variance for the spatial storage domain (b = .42, p < .05), and this moderator approached significance for the spatial CE (b = .47, p = .08) component. Larger effect sizes were associated with studies that controlled for RD/LI versus those studies that did not control for comorbid RD/LI. In contrast to the findings for the spatial WM domains, neither moderator variable (control for RD/LI, IQ) explained a significant amount of variance for either the verbal storage or verbal CE domains. Last, when the metaregressions were repeated without the studies designated as outliers, there were no changes to the findings in either the verbal or spatial storage domains or the verbal CE domain. However, the findings indicated that controlling for RD/LI did explain a significant amount of variance in the spatial CE domain (b = .69, p < .01) when the Williams et al. (2000) study was removed from the analysis. DISCUSSION
One of the advantages of meta-analysis is that it permits the pooling of results across studies and thus provides greater power to detect group differences. Overall, the findings suggest that WM processes are impaired in children with ADHD. Specifically, marked reductions in performance relative to normal controls were found for both the spatial storage and spatial CE WM components. In contrast, modest deficits were found for verbal storage and verbal CE WM domains. There are several possible explanations for the larger deficits ob-
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
served in spatial compared with verbal WM in ADHD. First, this pattern of findings may arise because spatial WM tasks tend to involve the right hemisphere (Kwon et al., 2002), which has been implicated in ADHD (see Giedd et al. [2001] for a review). It is also possible that the spatial tasks are simply more challenging than the verbal tasks because they may require processes that are less automated or familiar. Alternatively, it is possible that another disorder underlies the spatial WM weaknesses in ADHD. Approximately half of all children with ADHD may have motor difficulties consistent with developmental coordination disorder (Barkley et al., 1990; Piek et al., 1999; Pitcher et al., 2003). Developmental coordination disorder has been shown to be strongly associated with deficiencies in visuospatial processing (see Wilson and McKenzie [1998] for a review). Hence, future research is required to tease apart the nature of the WM impairments specifically associated with ADHD and whether they are attenuated by controlling for comorbid developmental coordination disorder. Heterogeneity statistics indicated that there was a moderate degree of inconsistency in the findings in each of the WM domains, except for verbal storage. Results of the metaregression analyses indicated that neither of the moderator variables (control for RD/LI, control for IQ) explained a significant amount of variation among studies in either the verbal storage or verbal CE domains of WM. Given the moderate degree of inconsistency of evidence in the verbal CE domain, this finding suggests that there must be other sources of variability within this domain that could not be addressed in the current study. For example, variation in findings in the verbal CE domain may be a result of the range of measures used to assess this construct. Although each of the verbal CE tasks required storage and manipulation, they differed in other factors such as linguistic or number knowledge. Hence, more research is needed that systematically examines how variation in verbal CE task components affects performance in children with ADHD. In contrast to the verbal domains, control for comorbid RD/LI was a significant moderator variable for both the spatial storage and spatial CE domains with greater effect sizes associated with studies that controlled for this confounder. This finding is surprising and somewhat difficult to explain because prior research has shown that children with RD/LI exhibit spatial storage
381
MARTI NUSS EN ET AL.
and spatial CE deficits independent of ADHD (Kaplan et al., 1998; McInnes et al., 2003). However, there are also data suggesting that spatial WM deficits are not characteristic of children with language impairments (Kushnir and Blake, 1996; Williams et al., 2000). More research is needed that examines the specific WM profiles associated with each disorder (ADHD, RD, LI) to better delineate the specific cognitive weaknesses associated with each disorder and areas of overlap. Nonetheless, the results provide support for WM deficits in ADHD, which in turn is consistent with both neuropsychological and neuroimaging data implicating frontostriatal dysfunctions in ADHD (Aman et al., 1998; Durston, 2003; Giedd et al., 2001) and recent findings linking ADHD with abnormalities in the dopamine system (Maher et al., 2002; Misener et al., 2004) as WM performance is highly dependent on prefrontal regions of the brain (Braver et al., 1997; Rypma and D’Esposito, 1999) and can be modulated by catecholamines (Bedard et al., 2004; Mattay et al., 2000). Future research should continue to explore the nature and severity of WM deficits in children with ADHD while also controlling for potential confounds such as comorbid motor, psychiatric, and/or learning disorders to examine the specificity of WM deficits to ADHD. Moreover, given that frontostriatal and cerebellar brain regions are implicated in ADHD (Durston, 2003) and in WM (Fletcher and Henson, 2001), it would be helpful to use neuroimaging techniques to examine whether alterations in the structure or function of these brain regions are related to subnormal performance on WM tasks in children with ADHD. Finally, further studies are needed that address the issue of symptom dimensions by investigating whether unique WM profiles are associated with specific symptom clusters. Limitations
Although a rigorous approach was used to review studies, this meta-analysis is constrained by the limitations that were inherent in the published study data. First, we were unable to examine whether comorbid psychiatric disorders or ADHD subtype differences moderated WM performance because too few studies examined these variables. Similarly, although there was variability in diagnostic criteria, the number of studies within each diagnostic category (e.g., DSM-IV, DSM-III-R, proxy ratings) was small, and therefore
382
this potentially confounding factor could not be analyzed statistically. Second, only published studies were included in the meta-analysis. Publication bias, which appeared to be evident in the spatial storage domain, may have increased our chances of finding larger effect sizes because unpublished studies tend to have smaller effect sizes than those that are published (Rosenthal, 1991). Overall, our findings should be interpreted with caution and regarded as exploratory in nature. Clinical Implications
Deficits in WM are known to compromise academic achievement (Gathercole and Pickering, 2000; Gathercole et al., 2004; Jarvis and Gathercole, 2003). Hence, the finding that children with ADHD exhibit moderate to large deficits in multiple components of WM has important implications for clinicians. Normal academic progress may be constrained by WM limitations because many typical academic activities depend heavily on WM (e.g., arithmetic problem solving, reading comprehension, text generation) (Daneman and Carpenter, 1980; Kellogg, 2001; Passolunghi and Siegel, 2001; Swanson and Berninger, 1996). Hence, it is possible that poor academic progress in children with ADHD may be the result of WM deficiencies rather than a direct consequence of the behavioral symptoms of inattention and/or hyperactivity-impulsivity (Rapport et al., 1999). WM constraints may also limit the efficacy of cognitively based treatment programs that are designed to target the behavioral symptoms of ADHD. In which case, specific accommodations may be required to help the child more easily encode, access, and retrieve information in an organized fashion. It is possible that providing support for WM limitations may help to reduce functional impairments in children with ADHD. According to Rosenshine (1997), effective teaching strategies such as teaching information in small steps followed with guided practice and using scaffolds such as cue cards may be successful because they appear to reduce the load on WM. Hence, these teaching strategies may facilitate academic success in children with ADHD. In addition, Dawson and Guare (2004) suggest that children with WM weaknesses may benefit from external support systems (e.g., visual cues, checklists, coaching) to help them remember specific goals and procedures. Additional research is needed that examines the relationship between WM limitations and
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05
WORKING MEMORY IMP AI RMENTS IN ADHD
academic achievement in children with ADHD to better understand the role that WM may play in the scholastic weaknesses exhibited by many children with ADHD (Barkley, 1990). Disclosure: Dr. Tannock has received research funding from, is a consultant for, and is on the speaker’s bureau for Eli Lilly. She is also on the advisory board for Shire and has received funding (for placebo control) from Novartis. The other authors have no financial relationships to disclose. REFERENCES References marked with an asterisk indicate studies included in the metaanalysis. *Adams JW, Snowling MJ (2001), Executive function and reading impairments in children reported by their teachers as Ôhyperactive.Õ Br J Dev Psychol 19:293–306 Aman CJ, Roberts RJ, Pennington BF (1998), A neuropsychological examination of the underlying deficit in attention deficit hyperactivity disorder: frontal lobe versus right parietal lobe theories. Dev Psychol 34: 956–969 Arnsten AFT (2001), Dopaminergic and noradrenergic influences on cognitive functions mediated by prefrontal cortex. In: Stimulant Drugs and ADHD: Basic and Clinical Neuroscience, Solanto MV, Arnsten AF, Castellanos FX, eds. London: Oxford University Press, pp 185–208 Awh E, Anllo-Vento L, Hillyard SA (2000), The role of spatial selective attention in working memory for locations: evidence from event-related potentials. J Cogn Neurosci 12:840–847 Baddeley A (1986), Working Memory. Oxford, England: Oxford University Press Baddeley A (1996), The fractionation of working memory. Proc Natl Acad Sci U S A 93:13468–13472 Baddeley A, Gathercole SE, Papagno C (1998), The phonological loop as a language learning device. Psychol Rev 105:158–173 Barkley R (1990), Attention-Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment. New York: Guilford Barkley R, DuPaul GJ, McMurray MB (1990), Comprehensive evaluation of attention deficit disorder with and without hyperactivity as defined by research criteria. J Consult Clin Psychol 58:775–789 Barkley R (1997), Behavioral inhibition, sustained attention, and executive function: constructing a unified theory of ADHD. Psychol Bull 121:65–94 *Barkley RA, Edwards G, Laneri M, Fletcher K, Metevia L (2001), Executive functioning, temporal discounting, and sense of time in adolescents with attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). J Abnorm Child Psychol 29:541–556 Barnett R, Maruff P, Vance A et al. (2001), Abnormal executive function in attention deficit hyperactivity disorder: the effect of stimulant medication and age on spatial working memory. Psychol Med 31:1107–1115 Bedard AC, Martinussen R, Ickowicz A, Tannock R (2004), Methylphenidate improves visual-spatial memory in children with attention deficit hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 43: 260–268 Biederman J, Faraone SV (2002), Current concepts on the neurobiology of ADHD. J Atten Disord 6:S7–16 Borenstein M, Rothstein H (2001) Comprehensive Meta-Analysis: Version 1.0. Englewood, NJ: Biostat Braver TS, Cohen JD, Nystrom LE, Jonides J, Smith EE, Noll DC (1997), A parametric study of prefrontal cortex involvement in human working memory. Neuroimage 5:49–62 Bunge SA, Ochsner KN, Desmond JE, Glover GH, Gabrieli JD (2001), Prefrontal regions are involved in keeping information in and out of mind. Brain 124:2074–2086
J. AM. ACAD. CH ILD ADO LE SC. PSY CH IAT RY, 44:4, APR IL 200 5
*Cairney S, Maruff P, Vance A, Barnett R, Luk E, Currie J (2001), Contextual abnormalities of saccadic inhibition in children with attention deficit hyperactivity disorder. Exp Brain Res 141:507–518 Castellanos FX, Lee PP, Sharp W et al. (2002), Developmental trajectories of brain volume abnormalities in children and adolescents with attentiondeficit/hyperactivity disorder. JAMA 288:1740–1748 Cohen J (1988), Statistical Power Analysis for the Behavioral Sciences. rev. ed. New York: Academic Daneman M, Carpenter PA (1980), Individual differences in working memory and reading. J Verb Learn Verb Behav 19:450–466 Daneman M, Merikle PM (1996), Working memory and language comprehension: a meta-analysis. Psychon Bull Rev 3:422–433 Dawson P, Guare R (2004), Executive Skills in Children and Adolescents: A Practical Guide to Assessment and Intervention. New York: Guilford DerSimonian R, Laird N (1986), Meta-analysis in clinical trials. Control Clin Trials 7:177–188 Durston S (2003), A review of the biological bases of ADHD: what have we learned from imaging studies? Ment Retard Dev Disabil Res Rev 9:184–195 Egger M, Smith GD, Schneider M, Minder C (1997), Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634 Fletcher PC, Henson RNA (2001), Frontal lobes and human memory: insights from functional neuroimaging. Brain 124:849–881 Gathercole SE, Pickering SJ (2000), Working memory deficits in children with low achievements in the national curriculum at 7 years of age. Br J Educ Psychol 70:177–194 Gathercole SE, Pickering SJ, Knight C, Stegman Z (2004), Working memory skills and educational attainment: evidence from national curriculum assessments at 7 and 14 years of age. Appl Cogn Psychol 18:1–16 Giedd JN, Blumenthal J, Molloy E, Castellanos FX (2001), Brain imaging of attention deficit hyperactivity disorder. Ann N Y Acad Sci 931:33–49 Goldman-Rakic PS, Castener SA, Svensson TH, Siever LJ, Williams GV (2004), Targeting the dopamine D (1) receptor in schizophrenia: insights for cognitive dysfunction. Psychopharmacol (Berl) 174:3–16 Gottwald B, Mihajlovic A, Wilde B, Mehdorn HM (2003), Does the cerebellum contribute to specific aspects of attention? Neuropsychologia 41:1452–1460 Higgins JPT, Thompson SG (2002), Quantifying heterogeneity in a metaanalysis. Stat Med 21:1539–1558 Higgins JPT, Thompson SG, Deeks JJ, Altman DG (2003), Measuring inconsistency in meta-analyses. BMJ 327:557–560 Hoskyn M, Swanson HL (2003), The relationship between working memory and writing in younger and older adults. Reading Writing 16: 759–784 Jarvis HL, Gathercole SE (2003), Verbal and non-verbal working memory and achievements on national curriculum tests at 11 and 14 years. Educ Child Psychol 20:123–140 *Kalff AC, Hendriksen JGM, Kroes M et al. (2002), Neurocognitive performance of 5- and 6-year old children who met criteria for attention deficit/hyperactivity disorder at 18 months follow-up: results from a prospective longitudinal study. J Abnorm Child Psychol 30:589–598 *Kaplan BJ, Dewey D, Crawford SG, Fisher GC (1998), Deficits in longterm memory are not characteristic of ADHD. J Clin Exp Neuropsychol 20:518–528 *Karatekin C, Asarnow RF (1998), Working memory in childhood-onset schizophrenia and attention-deficit/hyperactivity disorder. Psychiatry Res 80:165–176 Kellogg RT (2001), Competition for working memory among writing processes. Am J Psychol 114:175–191 *Kempton S, Vance A, Maruff P, Luk E, Costin J, Pantelis C (1999), Executive function and attention deficit hyperactivity disorder: stimulant medication and better executive function performance in children. Psychol Med 29:527–538 *Kerns K, McInerney RJ, Wilde NJ (2001), Time reproduction, working memory, and behavioral inhibition in children with ADHD. Neuropsychol Dev Cogn Sect C Child Neuropsychol 7:21–31 Kondo H, Morishita M, Osaka N, Osaka M (2004), Functional roles of the cingulo-frontal network in performance on working memory. Neuroimage 21:2–14
383
MARTI NUSS EN ET AL.
*Kuntsi J, Oosterlaan J, Stevenson J (2001), Psychological mechanisms in hyperactivity: I Response inhibition deficit, working memory impairment, delay aversion or something else? J Child Psychol Psychiatry 42:199–210 Kushnir CC, Blake J (1996), The nature of the cognitive deficit in specific language impairment. First Language 16:21–40 Kwon H, Reiss AL, Menon V (2002), Neural basis of protracted developmental changes in visuo-spatial working memory Proc Natl Acad Sci U S A 99:13336–13341 Lalonde R, Strazielle C (2003), The effects of cerebellar damage on maze learning in animals. Cerebellum 2:300–309 Lewis SJ, Dove A, Robbins TW, Barker RA, Owen AM (2004), Striatal contributions to working memory: a functional magnetic imaging study in humans. Eur J Neurosci 19:755–760 Levy F, Swanson JM (2001), Timing, space, and ADHD: the dopamine theory revisited. Aust N Z J Psychiatry 35:504–511 Lipsey MW, Wilson DB (2001), Practical Meta-Analysis. Thousand Oaks, CA: Sage Maher BS, Marazita ML, Ferrell RE, Vanyukov MM (2002), Dopamine system genes and attention deficit hyperactivity disorder: a meta-analysis. Psychiatr Genet 12:207–215 Mattay VS, Callicott JH, Bertolino A et al. (2000), Effects of dextroamphetamine on cognitive performance and cortical activation. Neuroimage 12L:268–275 *Mariani MA, Barkley RA (1997), Neuropsychological and academic functioning in preschool boys with attention deficit hyperactivity disorder. Dev Neuropsychol 13:111–129 *Mataro M, Garcia-Sanchez C, Junque C, Estevez-Gonzalez A, Pujol J (1997), Magnetic resonance imaging measurement of the caudate nucleus in adolescents with ADHD and its relationship with neuropsychological and behavioural measures. Arch Neurol 54:963–968 *McInnes A, Humphries T, Hogg-Johnson S, Tannock R (2003), Listening comprehension and working memory are impaired in attention-deficit hyperactivity disorder irrespective of language impairment. J Abnorm Child Psychol 31:427–443 Mehta MA, Owen A, Shakian BJ, Mavaddat N, Pickard JD, Robbins TW (2000), Methylphenidate enhances working memory by modulating discrete frontal and parietal lobe regions in the human brain. J Neurosci 20:RC65:1–6 Misener VL, Luca P, Azeke O et al. (2004), Linkage of the dopamine receptor D1 gene to attention-deficit/hyperactivity disorder. Mol Psychiatry 9:500–509 Miyake A, Shah P (1999), Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York: Cambridge University Press *Norrelgen F, Lacerda F, Forssberg H (1999), Speech discrimination and phonological working memory in children with ADHD. Dev Med Child Neurol 41:335–339 *Oie M, Sundet K, Rund BR (1999), Contrasts in memory functions between adolescents with schizophrenia or ADHD. Neuropsychologia 37:1351–1358 Passolunghi MC, Siegel LS (2001), Short-term memory, working memory, and inhibitory control in children with difficulties in arithmetic problem solving. J Exp Child Psychol 80:44–57 Pennington BF, Ozonoff S (1996), Executive functions and developmental psychopathology. J Child Psychol Psychiatry 37:51–87 Piek JP, Pitcher TM, Hay DA (1999), Motor coordination and kinaesthesis in males with attention-deficit hyperactivity disorder. Dev Med Child Neurol 41:159–165 Pitcher TM, Piek JP, Hay DA (2003), Fine and gross motor ability in males with ADHD. Dev Med Child Neurol 45:525–535 Rapport MD, Scanlan S, Denney CB (1999), Attention-deficit/hyperactivity disorder and scholastic achievement: a model of dual developmental pathways. J Child Psychol Psychiatry 40:169–184 Rapport MD, Chun K, Shore G, Isaacs P (2001), A conceptual model of child psychopathology: implications for understanding attention deficit hyperactivity disorder and treatment efficacy. J Clin Child Psychol 30:48–58 Rosenshine B (1997), Advances in research on instruction. In: Issues in Educating Students With Disabilities, Lloyd JW, Kameanui EJ, Chard D, eds. Mahwah, NJ: Erlbaum, pp 197–221
384
Rosenthal R (1991), Meta-analysis: a review. Psychosom Med 53:247–271 *Rucklidge J, Tannock R (2002), Neuropsychological profiles of adolescents with ADHD: effects of reading difficulties and gender. J Child Psychol Psychiatry 43:988–1003 Rypma B, D’Esposito M (1999), The roles of the prefrontal brain regions in components of working memory: effects of memory load and individual differences. Proc Natl Acad Sci U S A 96:6558–6563 *Schmitz M, Cadore L, Paczko M et al. (2002), Neuropsychological performance in DSM-IV ADHD subtypes: an exploratory study with untreated adolescents. Can J Psychiatry 47:863–869 Schweitzer JB, Faber TL, Grafton ST, Tune LE, Hoffmann JM, Kilts CD (2000), Alterations in the functional anatomy of working memory in adult attention deficit hyperactivity disorder. Am J Psychiatry 157:278–280 *Seidman LJ, Biederman J, Faraone SV, Weber W, Mennin D, Jones J (1997), A pilot study of neuropsychological function in girls with ADHD. J Am Acad Child Adolesc Psychiatry 36:366–373 *Shallice T, Marzocchi GM, Coser S, Del Savio M, Meuter RF, Rumiati R (2002), Executive function profile of children with attention deficit hyperactivity disorder. Dev Neuropsychol 21:43–71 Smith EE, Jonides J (1999), Storage and executive processes in the frontal lobes. Science 283:1657–1661 StataCorp (2003), Stata Statistical Software: Release 8.0. College Station, TX: Stata Corporation *Stevens J, Quittner AL, Zuckerman JB, Moore S (2002), Behavioral inhibition, self- regulation of motivation, and working memory in children with attention-deficit hyperactivity disorder. Dev Neuropsychol 21:117–139 Swanson HL, Berninger VW (1996), Individual differences in children’s working memory and writing skill. J Exp Child Psychol 63:358–385 Swanson HL (1999), Reading comprehension and working memory in learning-disabled readers: is the phonological loop more important than the executive system? J Exp Child Psychol 72:1–31 Swanson HL, Howell M (2001), Working memory, short-term memory, and speech rate as predictors of children’s reading performance at different ages. J Educ Psychol 93:720–734 Tannock R, Ickowicz A, Schachar R (1995), Differential effects of methylphenidate on working memory in ADHD children with or without comorbid anxiety. J Am Acad Child Adolesc Psychiatry 34:886–896 *Toplak ME, Rucklidge JJ, Hetherington R, John SCF, Tannock R (2003), Time perception deficits in attention-deficit/hyperactivity disorder and comorbid reading difficulties in child and adolescent samples. J Child Psychol Psychiatry 44:888–903 *Tripp G, Ryan J, Peace K (2002), Neuropsychological functioning in children with DSM-IV combined type attention deficit hyperactivity disorder. Aust N Z J Psychiatry 36:771–779 Wager TD, Smith EE (2003), Neuroimaging studies of working memory: a meta-analysis. Cogn Affect Behav Neurosci 3:255–274 *Warner-Rogers J, Taylor A, Taylor E, Sandberg S (2000), Inattentive behavior in childhood: epidemiology and implications for development. J Learn Disabil 33:520–536 Wechsler D (1999), Wechsler Intelligence Scale for Children. New York: Psychological Corporation *Wiers RW, Gunning WB, Sergeant JA (1998), Is a mild deficit in executive functions in boys related to childhood ADHD or to parental multigenerational alcoholism? J Abnorm Child Psychol 26:415–430 *Willcutt EG, Pennington BF, Boada R et al. (2001), A comparison of the cognitive deficits in reading disability and attention-deficit/hyperactivity disorder. J Abnorm Psychol 110:157–172 *Williams D, Stott CM, Goodyer IM, Sahakian BJ (2000), Specific language impairment with or without hyperactivity: neuropsychological evidence for frontostriatal dysfunction. Dev Med Child Neurol 42:368–375 Wilson PH, McKenzie BE (1998), Information processing deficits associated with developmental coordination disorder: a meta-analysis of research findings. J Child Psychol Psychiatry 29:829–840 Zeiner P, Bryhn G, Bjercke C, Truyen K, Strand G (1999), Response to methylphenidate in boys with attention-deficit hyperactivity disorder. Acta Paediatr 88:298–303
J. AM . ACAD. CH ILD ADOLESC. PSY CH IATRY, 44:4, APRIL 20 05